# US 2025/0148745 A1 · Enhanced object detection for autonomous vehicles based on field view > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0148745 A1 - Document ID: US20250148745A1 - Status: Published application - Kind: A1 - Application number: 19/018,362 - Filed: 2025-01-13 - Published: 2025-05-08 - Earliest claimed: 2018-12-04 - Assignees: Tesla, Inc. - Applicants: Tesla, Inc. - Inventors: Anting Shen; Romi Phadte; Gayatri Joshi - Web page: https://patentgrep.com/patent/US20250148745A1 ## CPC - G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI] - G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)} - G05D 1/0251: {extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision (stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593)} - G05D 1/0253: {extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow (determining position or orientation from images G06T7/70)} - G05D 1/227: Handing over between remote control and on-board control; Handing over between remote control arrangements - G05D 1/228: Command input arrangements located on-board unmanned vehicles - G05D 1/2435: {Extracting 3D information} - G05D 1/246: using environment maps, e.g. simultaneous localisation and mapping [SLAM] - G05D 1/2465: {using a 3D model of the environment} - G06F 18/211: Selection of the most significant subset of features - G06F 18/254: {of classification results, e.g. of results related to same input data} - G06V 10/809: {of classification results, e.g. where the classifiers operate on the same input data} - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems and methods for enhanced object detection for autonomous vehicles based on field of view. An example method includes obtaining an image from an image sensor of one or more image sensors positioned about a vehicle. A field of view for the image is determined, with the field of view being associated with a vanishing line. A crop portion corresponding to the field of view is generated from the image, with a remaining portion of the image being downsampled. Information associated with detected objects depicted in the image is outputted based on a convolutional neural network, with detecting objects being based on performing a forward pass through the convolutional neural network of the crop portion and the remaining portion. ## Claims ### Claim 1 A method comprising: obtaining, by a processor, an image captured by an imaging device during operation of a vehicle; determining, by the processor, a first region of the image and a second region of the image, the first region corresponding to a priority field-of-view and the second region corresponding to a remaining field-of-view; generating, by the processor, a first portion of the image corresponding to the first region and a second portion of the image corresponding to the second region, in response to generating the second portion of the image, downsampling the second portion of the image; and in response to downsampling the second portion of the image, providing, by the processor, the first portion of the image and the second portion of the image to a neural network to cause the neural network to generate an output indicating at least one detection of an object depicted within the image. ### Claim 2 (depends on 1) The method of claim 1, further comprising: determining the priority field-of-view based on attributes associated with operation of the vehicle and one or more heuristics, wherein determining the first region and the second region comprises: determining the first region and the second region based on the attributes associated with the operation of the vehicle and the one or more heuristics. ### Claim 3 (depends on 1) The method of claim 1, wherein obtaining the image comprises: obtaining the image in response to the image being captured by a camera associated with the vehicle. ### Claim 4 (depends on 1) The method of claim 1, wherein obtaining the image comprises: obtaining the image in response to the image being captured by a camera positioned relative to the vehicle such that the camera is oriented in a forward direction relative to the vehicle. ### Claim 5 (depends on 1) The method of claim 1, further comprising: obtaining data generated by an inertial measurement unit during operation of the vehicle, wherein providing the first portion of the image and the second portion of the image to the neural network comprises: providing the first portion of the image, the second portion of the image, and the data generated by the inertial measurement unit to the neural network to cause the neural network to generate the output indicating the at least one detection. ### Claim 6 (depends on 1) The method of claim 1, further comprising: obtaining map data about one or more lanes along which the vehicle is operating, wherein providing the first portion of the image and the second portion of the image to the neural network comprises: providing the first portion of the image, the second portion of the image, and the map data to the neural network to cause the neural network to detect at least one object depicted within the image. ### Claim 7 (depends on 6) The method of claim 6, wherein the map data indicates one or more aspects of the one or more lanes. ### Claim 8 (depends on 1) The method of claim 1, further comprising: in response to downsampling the second portion of the image, aligning the first portion of the image with the second portion of the image. ### Claim 9 (depends on 1) The method of claim 1, further comprising: determining, by the processor, a first detection of an object and a second detection of the object in the first portion of the image or the second portion of the image based on the output of the neural network; determining, by the processor, that the second detection of the object is a duplicate detection; and removing, by the processor, the first detection or the second detection. ### Claim 10 A system comprising one or more processors and non-transitory computer storage media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining an image captured by an imaging device during operation of a vehicle; determining a first region of the image and a second region of the image, the first region corresponding to a priority field-of-view and the second region corresponding to a remaining field-of-view; generating a first portion of the image corresponding to the first region and a second portion of the image corresponding to the second region, in response to generating the second portion of the image, downsampling the second portion of the image; and in response to downsampling the second portion of the image, providing the first portion of the image and the second portion of the image to a neural network to cause the neural network to generate an output indicating at least one detection of an object depicted within the image. ### Claim 11 (depends on 10) The system of claim 10, wherein the instructions further cause the one or more processors to: determine the priority field-of-view based on attributes associated with operation of the vehicle and one or more heuristics, wherein the instructions that cause the one or more processors to determine the first region and the second region cause the one or more processors to: determine the first region and the second region based on the attributes associated with the operation of the vehicle and the one or more heuristics. ### Claim 12 (depends on 10) The system of claim 10, wherein the instructions that cause the one or more processors to obtain the image cause the one or more processors to: obtain the image in response to the image being captured by a camera associated with the vehicle. ### Claim 13 (depends on 10) The system of claim 10, wherein the instructions that cause the one or more processors to obtain the image cause the one or more processors to: obtain the image in response to the image being captured by a camera positioned relative to the vehicle such that the camera is oriented in a forward direction relative to the vehicle. ### Claim 14 (depends on 10) The system of claim 10, wherein the instructions further cause the one or more processors to: obtain data generated by an inertial measurement unit during operation of the vehicle, wherein the instructions that cause the one or more processors to provide the first portion of the image and the second portion of the image to the neural network cause the one or more processors to: provide the first portion of the image, the second portion of the image, and the data generated by the inertial measurement unit to the neural network to cause the neural network to generate the output indicating the at least one detection. ### Claim 15 (depends on 10) The system of claim 10, wherein the instructions further cause the one or more processors to: obtain map data about one or more lanes along which the vehicle is operating, wherein the instructions that cause the one or more processors to provide the first portion of the image and the second portion of the image to the neural network cause the one or more processors to: provide the first portion of the image, the second portion of the image, and the map data to the neural network to cause the neural network to detect at least one object depicted within the image. ### Claim 16 (depends on 15) The system of claim 15, wherein the map data indicates one or more aspects of the one or more lanes. ### Claim 17 (depends on 10) The system of claim 10, wherein the instructions further cause the one or more processors to: in response to downsampling the second portion of the image, align the first portion of the image with the second portion of the image. ### Claim 18 (depends on 10) The system of claim 10, wherein the instructions further cause the one or more processors to: determine a first detection of an object and a second detection of the object in the first portion of the image or the second portion of the image based on the output of the neural network; determine that the second detection of the object is a duplicate detection; and remove the first detection or the second detection. ### Claim 19 A non-transitory computer storage media storing instructions that when executed by a system of one or more processors, cause the one or more processors to perform operations comprising: obtaining an image captured by an imaging device during operation of a vehicle; determining a first region of the image and a second region of the image, the first region corresponding to a priority field-of-view and the second region corresponding to a remaining field-of-view; generating a first portion of the image corresponding to the first region and a second portion of the image corresponding to the second region, in response to generating the second portion of the image, downsampling the second portion of the image; and in response to downsampling the second portion of the image, providing the first portion of the image and the second portion of the image to a neural network to cause the neural network to generate an output indicating at least one detection of an object depicted within the image. ### Claim 20 (depends on 19) The non-transitory computer storage media of claim 19, wherein the instructions further cause the one or more processors to: determine the priority field-of-view based on attributes associated with operation of the vehicle and one or more heuristics, wherein the instructions that cause the one or more processors to determine the first region and the second region cause the one or more processors to: determine the first region and the second region based on the attributes associated with the operation of the vehicle and the one or more heuristics.